Use of monte carlo simulations for establishing correlations and their uncertainties

Patent metadata

Field	Value
Publication number	US-9897714-B2
Application number	US-201414229254-A
Country	US
Kind code	B2
Filing date	Mar 28, 2014
Priority date	Nov 30, 2011
Publication date	Feb 20, 2018
Grant date	Feb 20, 2018

Abstract

A method and computer-readable medium for establishing an uncertainty for obtained values of a one-dimensional logging parameter mapped to a three-dimensional volume is disclosed. A relation is formed between the obtained values of the logging parameter and a volumetric parameter of the three-dimensional volume. A set of representative data points is obtained that relates the obtained values of the logging parameter to the volumetric parameter by binning the obtained values. A plurality of regression curves are then determined, wherein each regression curve is obtained by adding a random error to the set of representative data points to obtain a set of randomized data points and performing a regression analysis using the set of randomized data points. The plurality of regression curves are used to establish the uncertainty for the values of the logging parameter in the three-dimensional volume.

First claim

The invention claimed is:

1. A computer-implemented method of determining a correlation of a logging parameter of a volume of a formation to a volume parameter of the volume of the formation, comprising:
selecting, using a computer, a set of data points including the logging parameter at a plurality of depths over a depth interval in the formation and a volume parameter of the volume at the plurality of depths;
relating the volume parameter to the logging parameter at each of the plurality of depths;
for values of the logging parameter within a selected interval, estimating, using the computer, a standard deviation of the volume parameter and a standard deviation for the logging parameter;
using the estimated standard deviations to obtain, using the computer, a plurality of sets of randomized data points;
obtaining, using the computer, regression curves for each of the plurality of sets of randomized data points;
determining, using the computer, the uncertainties of the logging parameter in the volume from the plurality of regression curves;
determining, using the computer, a representation of the logging parameter of the formation in the volume from the volume parameter and the uncertainties of the logging parameter associated with the volume parameter; and
performing a drilling operation in the volume using the representation of the logging parameter of the formation in the volume.

2. The method of claim 1 , wherein obtaining the regression curve includes:
adding random errors to the selected set of data points to obtain the set of randomized data points; and
performing a regression analysis using the set of randomized data points.

3. The method of claim 2 , further comprising performing the regression analysis using a function selected from the group consisting of: (i) a linear function; (ii) a non-linear function; and (iii) a polynomial function.

4. The method of claim 2 , wherein the volume parameter is a seismic interval velocity, the method further comprising relating the volume parameter to the logging parameter by sorting a value of the logging parameter into one of a plurality of bins defined over a seismic interval velocity, the one of the plurality of bins including the value of the seismic interval velocity corresponding to the value of the logging parameter.

5. The method of claim 4 , wherein a data point for a selected bin has a y-value that is an average of the values of the logging parameter of the selected bin and an x-value that is a midpoint of the seismic interval velocity or an average of the values of the seismic interval velocity of the selected bin.

6. The method of claim 5 , wherein adding the random errors to the data point further comprises adding one random error to the y-value, wherein the one random error is selected from a normal distribution of the values of the logging parameter for the selected bin.

7. The method of claim 5 , wherein adding the random errors to the data point further comprises adding another random error to the x-value, wherein the another random error is selected from a normal distribution of the values of the seismic interval velocity for the selected bin.

8. The method of claim 4 , wherein performing the regression analysis further comprises weighting a randomized data point in the regression analysis based on a number of values of the logging parameter in a bin associated with the randomized data point.

9. The method of claim 1 , wherein the logging parameter is one of: (i) a density; (ii) an effective stress; and (iii) a pore pressure.

10. The method of claim 1 , wherein selecting the set of data points further comprises cross-plotting the values of the logging parameter against the volume parameter at a plurality of depths.

11. The method of claim 1 , further comprising determining realized data points of the logging parameter from the regression curve and determining at least one confidence interval using the realized data points.

12. A non-transitory computer-readable medium having a set of instructions stored therein that when executed by at least one processor enable the at least one processor to perform a method of determining a correlation of a logging parameter of a volume of a formation to a volume parameter of the volume of the formation, the method comprising:
selecting a set of data points including the logging parameter at a plurality of depths over a depth interval in the formation and a volume parameter of the volume at the plurality of depths;
relating the volume parameter to the logging parameter at each of the plurality of depths;
for values of the logging parameter within a selected interval, estimating a standard deviation for the volume parameter and a standard deviation for the logging parameter;
using the estimated standard deviations to obtain a plurality of sets of randomized data points;
obtaining a regression curve for each of the plurality of randomized data points;
determining the uncertainties of the logging parameter in the volume from the plurality of regression curves;
determining a representation of the logging parameter of the formation in the volume from the volume parameter and the uncertainties of the logging parameter associated with the volume parameter; and
performing a drilling operation in the volume using the representation of the logging parameter of the formation in the volume.

13. The computer-readable medium of claim 12 , wherein obtaining a regression curve of the plurality of regression curves includes:
adding random errors to the selected set of data points to obtain the set of randomized data points, and
performing a regression analysis using the set of randomized data points.

14. The computer-readable medium of claim 13 , further comprising performing the regression analysis using a function selected from the group consisting of: (i) a linear function; (ii) a non-linear function; and (iii) a polynomial function.

15. The computer-readable medium of claim 13 , wherein the volume parameter is a seismic interval velocity, the method further comprising selecting the set of data points by sorting the values of the logging parameter into a plurality of bins defined over a seismic interval velocity.

16. The computer-readable medium of claim 15 , wherein a data point for a bin has a y-value that is an average of the values of the logging parameter of the selected bin and an x-value that is a midpoint of the seismic interval velocity or an average of the values of the seismic interval velocity of the selected bin.

17. The computer-readable medium of claim 16 , wherein adding the random errors to the data point further comprises adding one random error to the y-value, wherein the one random error is selected from a normal distribution of the values of the logging parameter for the selected bin.

18. The computer-readable medium of claim 16 , wherein adding the random errors to the data point further comprises adding another random error to the x-value, wherein the another random error is selected from a normal distribution of the values of the seismic interval velocity of the selected bin.

19. The computer-readable medium of claim 15 , wherein performing the regression analysis further comprises weighting a randomized data point in the regression analysis based on a number of values of the logging parameter in a bin associated with the randomized data point.

20. The computer-readable medium of claim 13 , wherein the logging parameter is one of: (i) a density; (ii) an effective stress; and (iii) a pore pre

Assignees

• Holland Marc
• Van Der Zee Wouter E
• Baker Hughes A Ge Co Llc

Inventors

• Holland Marc
• Van Der Zee Wouter E

Classifications

• G01V1/50 Primary

  Analysing data · CPC title

• G01V2210/667

  Determining confidence or uncertainty in parameters · CPC title

• G01V3/18

  specially adapted for well-logging · CPC title

• E21B49/087

  Well testing, e.g. testing for reservoir productivity or formation parameters · CPC title

• E21B47/00

  Survey of boreholes or wells (monitoring pressure or flow of drilling fluid E21B21/08) · CPC title